Residential Behavioral Savings: An Analysis of Principal Electricity End Uses in British Columbia
Bibliographic record
Abstract
Research on energy savings in residential dwellings has been dominated by an engineering economics paradigm, in which economic agents adopt practices and technologies which are cost effective.This paper challenges this paradigm and reports on a detailed behavioral study done with residential customers.Using data collected from a survey of 1,437 residential customers, we apply the conditions, capacity and commitment model.The model was applied to six residential energy end uses:(1) space heating, (2) lighting, (3) domestic hot water, (4) washing appliances, (5) refrigeration and (6) consumer electronics.In each end use area, respondents were asked a series of scaled questions dealing with their level of satisfactionwith the service level for the end use (conditions); their ability to modify or change service levels (capacity); and the extent to which they performed energy efficient actions or behaviors (commitment).Using simple engineering algorithms, the study also estimated potential behavioral energy savings at the end use level.The study found that refrigerator and freezer temperature control, defrosting freezers, checking the hot water tank temperature and turning off the hot water tank while away from home were particularly effective means of saving energy in residential buildings.Somewhat less, but still effective means of saving energy in residential buildings include using cold water to wash clothes, air drying dishes, turning off outside lights and lights in empty rooms, using low wattage bulbs, night and day temperature setbacks, keeping part of the house cooler, draft proofing, installation of storm windows and unplugging computers and entertainment equipment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".